Edge AI for Real-Time Decision Making in IOT Networks

The proliferation of Internet of Things (IoT) devices and networks has led to an exponential increase in data generation at the network edge. Processing this data in real-time to enable rapid decision making presents significant challenges for traditional cloud-centric architectures. Edge AI, which involves deploying artificial intelligence algorithms directly on edge devices and gateways, has emerged as a promising solution to enable lowlatency analytics and decision making in IoT networks. This paper presents a comprehensive review and analysis of Edge AI techniques for real-time decision making in IoT environments. We examine the key components, algorithms, and architectures for Edge AI systems, as well as the challenges and opportunities in this rapidly evolving field. Through extensive experiments and case studies, we demonstrate how Edge AI can significantly improve response times, reduce bandwidth usage, enhance privacy, and enable new IoT applications across multiple domains including smart cities, industrial IoT, and autonomous systems. Our results show that Edge AI can reduce decision-making latency by up to 90% compared to cloud-only approaches while maintaining comparable accuracy. We conclude by discussing future research directions and the potential impact of Edge AI on the continued growth and evolution of IoT networks.